# Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results

arXiv: https://arxiv.org/abs/1706.03470 · HTML: https://mlsanity.com/paper/1706.03470/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2017.

## Built on by (most cited first, 11 of 11)

- [MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization](https://mlsanity.com/paper/2505.17745/) (2025)
- [Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions](https://mlsanity.com/paper/2102.02558/) (2021)
- [Learning to Transfer for Evolutionary Multitasking](https://mlsanity.com/paper/2406.14359/) (2024)
- [A Scalable Test Problem Generator for Sequential Transfer Optimization](https://mlsanity.com/paper/2304.08503/) (2023)
- [A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks](https://mlsanity.com/paper/2204.03297/) (2022)
- [Large Language Model as Meta-Surrogate for Data-Driven Many-Task Optimization: A Proof-of-Principle Study](https://mlsanity.com/paper/2503.08301/) (2025)
- [Learning Where, What and How to Transfer: A Multi-Role Reinforcement Learning Approach for Evolutionary Multitasking](https://mlsanity.com/paper/2511.15199/) (2025)
- [Self-adaptive Multi-task Particle Swarm Optimization](https://mlsanity.com/paper/2110.04473/) (2021)
- [Transfer of Knowledge through Reverse Annealing: A Preliminary Analysis of the Benefits and What to Share](https://mlsanity.com/paper/2501.15865/) (2025)
- [Residual Learning Inspired Crossover Operator and Strategy Enhancements for Evolutionary Multitasking](https://mlsanity.com/paper/2503.21347/) (2025)
- [In-Context Guidance: Learning Inter-Task Synergies via Numerical Foundational Models for Few-Shot Multitask Optimization](https://mlsanity.com/paper/2609.25836/) (2026)

## Abstract (arXiv)

In this report, we suggest nine test problems for multi-task single-objective optimization (MTSOO), each of which consists of two single-objective optimization tasks that need to be solved simultaneously. The relationship between tasks varies between different test problems, which would be helpful to have a comprehensive evaluation of the MFO algorithms. It is expected that the proposed test problems will germinate progress the field of the MTSOO research.